Martin Law

Distinct Factors Control Histone Variant H3.3 Localization at Specific Genomic Regions

The incorporation of histone H3 variants has been implicated in the epigenetic memory of cellular state. Using genome editing with zinc-finger nucleases to tag endogenous H3.3, we report genome-wide profiles of H3 variants in mammalian embryonic stem cells and neuronal precursor cells. Genome-wide patterns of H3.3 are dependent on amino acid sequence and change with cellular differentiation at developmentally regulated loci. The H3.3 chaperone Hira is required for H3.3 enrichment at active and repressed genes. Strikingly, Hira is not essential for localization of H3.3 at telomeres and many transcription factor binding sites. Immunoaffinity purification and mass spectrometry reveal that the proteins Atrx and Daxx associate with H3.3 in a Hira-independent manner. Atrx is required for Hira-independent localization of H3.3 at telomeres and for the repression of telomeric RNA. Our data demonstrate that multiple and distinct factors are responsible for H3.3 localization at specific genomic locations in mammalian cells.

ATR-X Syndrome Protein Targets Tandem Repeats and Influences Allele-Specific Expression in a Size-Dependent Manner

ATRX is an X-linked gene of the SWI/SNF family, mutations in which cause syndromal mental retardation and downregulation of α-globin expression. Here we show that ATRX binds to tandem repeat (TR) sequences in both telomeres and euchromatin. Genes associated with these TRs can be dysregulated when ATRX is mutated, and the change in expression is determined by the size of the TR, producing skewed allelic expression. This reveals the characteristics of the affected genes, explains the variable phenotypes seen with identical ATRX mutations, and illustrates a new mechanism underlying variable penetrance. Many of the TRs are G rich and predicted to form non-B DNA structures (including G-quadruplex) in vivo. We show that ATRX binds G-quadruplex structures in vitro, suggesting a mechanism by which ATRX may play a role in various nuclear processes and how this is perturbed when ATRX is mutated.

Combinatorial readout of histone H3 modifications specifies localization of ATRX to heterochromatin

Accurate read-out of chromatin modifications is essential for eukaryotic life. Mutations in the gene encoding X-linked ATRX protein cause a mental-retardation syndrome, whereas wild-type ATRX protein targets pericentric and telomeric heterochromatin for deposition of the histone variant H3.3 by means of a largely unknown mechanism. Here we show that the ADD domain of ATRX, in which most syndrome-causing mutations occur, engages the N-terminal tail of histone H3 through two rigidly oriented binding pockets, one for unmodified Lys4 and the other for di- or trimethylated Lys9. In vivo experiments show this combinatorial readout is required for ATRX localization, with recruitment enhanced by a third interaction through heterochromatin protein-1 (HP1) that also recognizes trimethylated Lys9. The cooperation of ATRX ADD domain and HP1 in chromatin recruitment results in a tripartite interaction that may span neighboring nucleosomes and illustrates how the histone-code is interpreted by a combination of multivalent effector-chromatin interactions.

Understanding α‐Globin Gene Regulation: Aiming to Improve the Management of Thalassemia

Abstract: Over the past 50 years, many advances in our understanding of the general principles controlling gene expression during hematopoiesis have come from studying the synthesis of hemoglobin. Discovering how the α‐ and β‐globin genes are normally regulated and documenting the effects of inherited mutations that cause thalassemia have played a major role in establishing our current understanding of how genes are switched on or off in hematopoietic cells. Previously, nearly all mutations causing thalassemia have been found in or around the globin loci, but rare inherited and acquired trans‐acting mutations are being found more often. Such mutations have demonstrated new mechanisms underlying human genetic disease. Furthermore, they are revealing new pathways in the regulation of globin gene expression that, in turn, may open up new avenues for improving the management of patients with common types of thalassemia.

Two new methods to fit models for network meta-analysis with random inconsistency effects

BackgroundMeta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects.MethodsThe model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components.ResultsWe illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses “ear discharge as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC.ConclusionsThe extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.

Extending DerSimonian and Laird's methodology to perform network meta‐analyses with random inconsistency effects

Network meta‐analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta‐analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi‐parametric, non‐iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.

A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects: Multivariate Network Meta-Analysis

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

The Hartung-Knapp modification for random-effects meta-analysis: A useful refinement but are there any residual concerns?

The modified method for random‐effects meta‐analysis, usually attributed to Hartung and Knapp and also proposed by Sidik and Jonkman, is easy to implement and is becoming advocated for general use. Here, we examine a range of potential concerns about the widespread adoption of this method. Motivated by these issues, a variety of different conventions can be adopted when using the modified method in practice. We describe and investigate the use of a variety of these conventions using a new taxonomy of meta‐analysis datasets. We conclude that the Hartung and Knapp modification may be a suitable replacement for the standard method. Despite this, analysts who advocate the modified method should be ready to defend its use against the possible objections to it that we present. We further recommend that the results from more conventional approaches should be used as sensitivity analyses when using the modified method. It has previously been suggested that a common‐effect analysis should be used for this purpose but we suggest amending this recommendation and argue that a standard random‐effects analysis should be used instead.

Paule-Mandel estimators for network meta-analysis with random inconsistency effects

Network meta‐analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta‐analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between‐study heterogeneity. Models for network meta‐analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Lairds univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta‐analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta‐analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood‐based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods.

Residual plots for linear regression models with censored outcome data: A refined method for visualizing residual uncertainty

ABSTRACT Residual plots are a standard tool for assessing model fit. When some outcome data are censored, standard residual plots become less appropriate. Here, we develop a new procedure for producing residual plots for linear regression models where some or all of the outcome data are censored. We implement two approaches for incorporating parameter uncertainty. We illustrate our methodology by examining the model fit for an analysis of bacterial load data from a trial for chronic obstructive pulmonary disease. Simulated datasets show that the method can be used when the outcome data consist of a variety of types of censoring.